An Extensive Review on Malware Classification Based on Classifiers

Kavitha, P.; Muruganantham, B.

doi:10.1007/978-981-16-3153-5_40

Cited by 2 publications

(1 citation statement)

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“…Stacking classi ers have been shown to be the useful in malware detection in recent researches. For example, the effectiveness of stacking classi ers in classifying malware datasets by analyzing hash codes showed good results and performed effectively [29]. They discovered that stacking classi ers beat separate techniques in terms of their e ciency metrics, implying that they have the potential to improve malware detection systems.…”

Section: Cic Dataset Autoencoder-decoders Binarymentioning

confidence: 99%

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Nallabala

2024

Preprint

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Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models.

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Section: Cic Dataset Autoencoder-decoders Binarymentioning

confidence: 99%